Cryptocurrency Exchange Simulation

Computational Economics. 2024. Vol. 64. P. 2585–2603.

Mansurov K., Semenov A., Dmitry Grigoriev, Radionov A., Ibragimov R.

In this paper, we consider the approach of applying state-of-the-art machine learning
algorithms to simulate some financial markets. In this case, we choose the crypto-
currency market based on the assumption that such markets more active today. As
a rule, they have more volatility, attracting riskier traders. Considering classic trad-
ing strategies, we also introduce an agent with a self-learning strategy. To model the
behavior of such agent, we use deep reinforcement learning algorithms, namely Deep
Deterministic policy gradient. Next, we develop an agent-based model with follow-
ing strategies. With this model, we will be able to evaluate the main market statistics,
named stylized-facts. Finally, we conduct a comparative analysis of results for con-
structed model with outcomes of previously proposed models, as well as with the
characteristics of real market. As a result, we conclude that our model with a self-
learning agent gives a better approximation to the real market than a model with clas-
sical agents. In particular, unlike the model with classical agents, the model with a
self-learning agent turns out to be not so heavy-tailed. Thus, we demonstrate that for
a complete understanding of market processes simulation models should take into
account self-learning agents that have a significant presence at modern stock markets.

Keywords: cryptocurrency, agent-based model, Reinforcement learning, Market simulations